Some researchers introduced a bidirectional training strategy, to enhance the standard RNN's ability to handle long-term dependent data sequences.
For example, Schuster and Paliwal [1]} generalized the causal structure of the RNNs to a non-causal learning model, coined bidirectional recurrent
neural network (BRNN). The causal RNNs' output at time stamp, \(t\)  does not depend on future values of inputs, but the BRNN's response at time stamp, \(t\)  depends on both the past and the future input values [2]}, [3]}. It is notable that when the BRNN hidden layers structured with LSTM units, the resulting architecture will be a Bidirectional LSTM (BLSTM). Hence, forming DNNs by stacking of several hidden layers of BRNN or BLTM can produce better generalized performance [4]}, [5]}, [6]}.
